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OpenMetadata

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OpenMetadata is the #1 open source data catalog tool with the all-in-one platform for data discovery, quality, governance, collaboration & more. Join our community to stay updated.

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Type
Data Catalog
Category
Data Quality
Pricing
Free (open source)
Deployment
Cloud or self-hosted
Last updatedOctober 12, 2026Open Source

Editor's Take

We recommend OpenMetadata for data teams of 5+ that want an open-source catalog combining data discovery, quality, governance, and collaboration without committing to proprietary catalog pricing. Its all-in-one scope makes it a strong fit for teams able to operate and customize open-source infrastructure, but the available context does not provide evidence to assess enterprise-scale adoption or total implementation cost.

— Egor Burlakov, Editor

Evaluate OpenMetadata

OpenMetadata Pricing
Plans, costs & free tier
Top Alternatives
Substitutes, and when to pick them
Best Data Catalogs
Ranked by public evidence
Data Catalogs landscape
9 published products in this market
Browse Data Quality
Every published tool in the category
Stack Recommender
Build a stack around OpenMetadata

Popular comparisons

vs Atlan & Alation
Head-to-head comparison
vs Alation
Head-to-head comparison
vs DataHub
Head-to-head comparison
vs Atlan
Head-to-head comparison
See all 7 OpenMetadata comparisons
vs Monte Carlo
Head-to-head comparison
vs Great Expectations
Head-to-head comparison
vs Soda
Head-to-head comparison

OpenMetadata: product and architecture

Our OpenMetadata review verdict: this is a strong choice for teams that want an open, unified metadata platform and are prepared to own the operational work that comes with it. It combines cataloging, discovery, governance, quality, observability, collaboration, and lineage in one platform, while remaining free and open source under the Apache 2.0 license. We recommend OpenMetadata for organizations that need broad metadata coverage across a growing data ecosystem and value control over their platform architecture more than a low-effort SaaS experience.

OpenMetadata’s positioning is unusually broad for a data-quality-category product. It is designed as a central metadata store for data sources across the ecosystem, with standardized schemas and APIs intended to connect discovery and governance work rather than isolate them in separate tools. The trade-off is clear: breadth can reduce the number of point products a team manages, but it also makes implementation, metadata ownership, and adoption more demanding.

Overview

OpenMetadata is an open and unified metadata platform for data discovery, observability, and governance. Its stated purpose is to give data practitioners one place to build and manage high-quality data assets at scale, covering data sources, pipelines, and data products rather than only one layer of the stack. The platform is built by Collate and by founders associated with Apache Hadoop, Apache Atlas, and Uber Databook.

For data engineers, the core value is a central metadata system that can consolidate information from multiple services. For analytics engineers, the practical payoff is a more discoverable environment in which assets can be documented, profiled, connected through lineage, and governed with consistent metadata. For data leaders, it provides a way to frame quality, collaboration, and governance as parts of the same operational system.

OpenMetadata should not be evaluated as a simple data catalog alone. Its repository describes it as an “Open Context Layer for Data and AI,” focused on trusted data context and business semantics for humans, AI assistants, and agents. That broader direction matters: teams buying only a lightweight search interface may find the platform’s scope larger than they need, while teams struggling with fragmented metadata may find the integrated approach compelling.

Public adoption indicators are meaningful but should be interpreted carefully. OpenMetadata reports 3,000+ enterprise deployments, 11,000+ open-source members, and an active community of thousands globally; its GitHub repository has 14,868 stars. These signals support the conclusion that the project has visible community traction, but they are not proof that every deployment has the same maturity, operating model, or enterprise support requirements.

Key Features and Architecture

OpenMetadata’s architecture centers on a central metadata store that integrates metadata from different sources in the data ecosystem. It uses standardized schemas and APIs, which is important because catalog, lineage, quality, and governance processes become less useful when each function maintains incompatible definitions of the same asset. This architecture makes OpenMetadata suitable for teams that need a shared metadata foundation instead of a collection of disconnected interfaces.

Key technical capabilities include:

  • Unified asset discovery: OpenMetadata is designed to provide a single source of truth for data sources, data pipelines, and data products. Its discovery objective is practical: help teams get the right data assets to the right people so they can perform their work without relying solely on tribal knowledge.

  • Metadata ingestion framework: The platform’s ingestion framework supports connectors for 100+ data services, with additional connectors added in every release. This connector breadth is valuable when a data estate spans multiple systems, but teams should still validate the exact services and metadata depth they require before committing.

  • Data lineage: OpenMetadata supports lineage as part of the platform’s metadata capabilities. This allows teams to represent relationships between assets and pipelines in the same environment used for discovery and governance, rather than making lineage a separate documentation exercise.

  • Data quality and observability: The platform includes data quality, observability, and profiling capabilities. These functions matter because metadata becomes more actionable when a user can assess not only what an asset is, but also whether it is being monitored and understood as a quality-relevant resource.

  • Governance and collaboration: Metadata versioning supports governance and people collaboration. Versioned metadata is particularly relevant when ownership, business semantics, documentation, and governance decisions change over time and must be managed as durable organizational knowledge.

  • Data contracts and business semantics: The project’s GitHub topics include data contracts, data collaboration, data discovery, data governance, and data lineage. Its repository description explicitly frames the product around trusted context and business semantics, which gives it a wider remit than asset indexing alone.

The project’s primary GitHub language is TypeScript, and its repository is licensed under Apache-2.0. The latest listed release is 1.13.3-release, dated 2026-07-31, while the repository was last pushed on 2026-08-13. These are useful maintenance signals for an evaluator, although version activity alone does not establish whether a particular connector, deployment topology, or governance workflow fits a team’s requirements.

Ideal Use Cases

OpenMetadata is best for a data organization that has outgrown informal documentation and needs one operating layer for metadata across discovery, governance, quality, and lineage. A team of 10 to 30 data engineers and analytics engineers supporting multiple business domains can use it to establish shared ownership and asset context without treating every catalog entry as an isolated documentation task. The platform’s 100+ service connectors make this especially relevant when the team has a heterogeneous estate rather than a single data system.

A second strong use case is a larger enterprise program where data leadership needs to connect governance work to operational metadata. OpenMetadata reports 3,000+ enterprise deployments, making enterprise usage part of its stated market evidence, while its open-source licensing gives organizations direct access to the software. This model suits organizations that want to control implementation decisions, establish their own metadata standards, and avoid making governance dependent on a proprietary platform license.

A third good fit is a team building trusted context around data products and pipelines. Because OpenMetadata includes data discovery, profiling, collaboration, observability, lineage, and metadata versioning, it can support a workflow in which practitioners discover an asset, understand its relationships, assess relevant quality context, and contribute to its governance. This is particularly useful where several groups—data engineering, analytics, governance, and business users—must work from the same definitions.

Do not use OpenMetadata if your real requirement is only a narrow, standalone testing framework or only a minimal data-quality check. Its scope is broader than that, and adopting a unified metadata platform requires decisions about metadata ownership, ingestion coverage, governance workflows, and ongoing platform operation. Avoid it if your team does not have the capacity to operate an open-source platform or if it expects a catalog to become complete without active stewardship.

We recommend OpenMetadata for teams that need metadata to become shared infrastructure rather than a side project. Choose a more specialized tool instead when the primary objective is a tightly bounded quality-testing workflow, because OpenMetadata’s broader platform capabilities will add evaluation and implementation surface area that may not produce proportional value.

Strengths & Trade-offs

In our evaluation, OpenMetadata’s strengths are real, but they are tied to a platform-oriented operating model. It is more compelling when discovery, governance, quality, observability, and collaboration need to reinforce each other. It is less compelling when a team wants one narrowly scoped function with minimal operational responsibility.

Pros:

  • Broad metadata scope in one platform: OpenMetadata combines discovery, governance, quality, observability, profiling, collaboration, lineage, and metadata versioning. This can reduce fragmentation between teams that would otherwise manage those concerns through disconnected processes.

  • Connector coverage for mixed environments: Its ingestion framework supports connectors for 100+ data services. That coverage is useful for organizations whose metadata is spread across several kinds of data sources and pipelines.

  • Open-source licensing: The Apache 2.0 license gives teams access to the software without a proprietary license cost for the open-source edition. This is attractive for organizations that need control over deployment and technical governance.

  • A standardized metadata foundation: OpenMetadata uses standardized schemas and APIs, which supports a more consistent metadata model across sources. This is a concrete advantage for teams trying to make governance and discovery work across organizational boundaries.

  • Visible project activity and community signals: The repository has 14,868 GitHub stars, its latest listed release is 1.13.3-release, and it was last pushed on 2026-08-13. These are public indicators that the project is active and visible, though they should not replace implementation diligence.

Cons:

  • Broad scope creates implementation overhead: OpenMetadata is not merely a catalog interface; it spans governance, quality, observability, collaboration, lineage, and versioning. Teams must define ownership and workflows across those areas or risk deploying a technically capable platform with incomplete metadata.

  • Connector count does not guarantee fit: Support for 100+ data services is valuable, but OpenMetadata's public documentation does not establish the depth of every connector’s metadata coverage for a specific environment. Buyers must validate the exact systems and workflows they need.

  • Self-management carries real operational cost: The open-source model removes a software license fee, not the effort required to deploy, upgrade, maintain, and govern the platform. This is a material limitation for small teams without platform engineering capacity.

  • It is weak as a narrow point solution: Teams looking only for standalone data testing or a minimal monitoring workflow may find OpenMetadata unnecessarily expansive. Its value depends on adopting a unified metadata approach, not merely switching on one feature.

OpenMetadata pricing

Starting at
Free (open source)
Free access
Open source

View full OpenMetadata pricing intelligence →

Alternatives to OpenMetadata

The reviewed substitutes for OpenMetadata among the data catalogs, and what would make each one the better answer.

Direct alternatives

Reviewed substitutes: products bought for the same job, where a team picks one.

Alation
Two products of the same kind answering one purchase. Independent 2026 buyer's guides and vendor head-to-heads compare them directly, and a team adopts one.Applies to: Choosing between two products of the same kind for one job.
DataHub
Two open-source data catalogs in the same product class, covering discovery, lineage and governance. Both vendors and independent reviewers publish direct head-to-heads, and teams pick one.Applies to: Selecting one open-source data catalog.
Collibra
Two products of the same kind on one reviewed shortlist, answering the same purchase. data catalog guides and vendor comparisons rank these products together, and a team adopts one, so the comparison is a substitution.Applies to: Choosing between these two for the data catalog governance decision.
Castor
Two products of the same kind on one reviewed shortlist, answering the same purchase. data catalog guides and vendor comparisons rank these products together, and a team adopts one, so the comparison is a substitution.Applies to: Choosing between these two for the data catalog governance decision.
Secoda
Two products of the same kind on one reviewed shortlist, answering the same purchase. data catalog guides and vendor comparisons rank these products together, and a team adopts one, so the comparison is a substitution.Applies to: Choosing between these two for the data catalog governance decision.

Other approaches

A different approach to the same problem. Each substitutes only for the workload named beside it.

Atlan
Both catalogue data assets with lineage and governance, and the decision is commercial platform against self-hosted open source. Atlan publishes a direct comparison and independent guides put them on one shortlist.Applies to: Choosing a data catalog, weighing a managed commercial platform against self-hosting.
Monte Carlo
A catalog organises assets for discovery and governance; an observability platform detects incidents and monitors reliability. Lineage and metadata overlap enough that vendors on both sides publish guidance on whether one covers the other, and teams with a trust problem rather than a discovery problem do buy the observability platform instead of a catalog. The decision is which capability the organisation needs first, and whether one product covers both.Applies to: Whether discovery and reliability need two products, or one platform can carry both.

Related technologies

Normally used together rather than chosen between, so these are not alternatives.

Great Expectations
OpenMetadata integrates Great Expectations rather than replacing it: Great Expectations defines and runs validations, OpenMetadata catalogues the results beside lineage and ownership. The integration is documented by both projects.Applies to: Whether a data catalog removes the need for a separate validation framework, or displays its results.
Soda
Soda defines and runs data quality checks; OpenMetadata catalogues assets and displays quality results beside lineage and ownership. The catalog reports quality it does not itself produce, which is the same division as any catalog against a validation tool.Applies to: Whether a data catalog removes the need for a separate checks tool, or surfaces its results.

Head to head

See detailed alternatives analysis

If you are evaluating OpenMetadata alternatives, you are likely looking for a metadata management platform that fits your team's specific needs around data discovery, governance, lineage, and observability. OpenMetadata is a strong open-source option built under the Apache 2.0 license, offering a unified metadata platform with support for 120+ native connectors, data quality checks, lineage tracking, and team collaboration. However, depending on your organization's scale, budget, cloud strategy, or need for managed services, other platforms may be a better fit. Below is a detailed look at the leading OpenMetadata alternatives and how they compare.

Top Alternatives Overview

Alation is an enterprise data intelligence platform that combines data cataloging, governance, lineage, and AI-powered search. Alation positions itself as an agentic data intelligence platform with 120+ native connectors spanning warehouses, BI tools, and cloud services. It is recognized as a Gartner Magic Quadrant Leader for Metadata Management Solutions. Alation focuses on enabling self-service analytics and data governance for large organizations, with features like natural language search, automated metadata discovery, and a SQL editor called Compose. Unlike OpenMetadata's open-source model, Alation is a commercial enterprise product with pricing that requires contacting their sales team.

DataHub is another prominent open-source metadata platform, licensed under Apache 2.0, and originally developed at LinkedIn. DataHub focuses on enterprise context management, providing data discovery, observability, and governance capabilities. It supports 70+ native integrations and offers column-level, cross-system lineage tracking. DataHub also offers a commercial managed service called DataHub Cloud with additional enterprise features like AI-powered discovery and data quality monitoring. Notable adopters include Netflix, Visa, Slack, and Pinterest.

Atlan is a modern data workspace that combines data catalog, governance, and collaboration capabilities. It positions itself as an active metadata platform with automated tag propagation across lineage paths and bidirectional sync with platforms like Snowflake and Databricks. Atlan offers a freemium model and is recognized as both a Gartner Magic Quadrant Leader and Forrester Wave Leader.

Collibra is a cloud-based data governance platform focused on enabling organizations to gain visibility into their data, automate compliance processes, and manage data governance at enterprise scale. Collibra is often positioned as a solution for regulated industries that require strong policy management and compliance documentation. Pricing requires contacting their sales team.

Great Expectations takes a different approach as an open-source data quality and validation framework. Rather than serving as a full metadata catalog, it lets teams define, execute, and document expectations about their data. It is best suited for teams that need robust data validation and testing as part of their pipeline workflows.

Elementary is a dbt-native data observability solution that provides automated anomaly detection, data lineage, and test results visualization directly within dbt projects. It is available as both a self-hosted open-source tool and a cloud service with premium features.

Architecture and Approach Comparison

OpenMetadata uses an API-first and schema-first architecture with only four core system components, which the project highlights as making it easier to deploy, operate, and upgrade compared to more complex alternatives. It stores metadata in a centralized repository and uses standardized schemas and APIs to integrate metadata from different sources across the data ecosystem. OpenMetadata supports data discovery, quality, observability, profiling, collaboration, lineage, and governance -- all within a single platform.

Alation's architecture is built around a Behavioral Analysis Engine that uses machine learning to interpret natural language searches and surface the most relevant data assets. It emphasizes a people-first approach to governance and supports active metadata that auto-extracts and integrates metadata from connected sources. Alation's architecture predates the modern Snowflake/Databricks/dbt era, and some organizations report that its integrations with these newer platforms require more manual configuration compared to purpose-built alternatives.

DataHub takes an extensible metadata platform approach, built around a metadata graph that supports flexible entity modeling. Its open-source core supports over 70 native integrations, and the platform emphasizes automated data quality assessments and AI-driven anomaly detection. DataHub's architecture enables organizations to model custom metadata entities and relationships, making it highly adaptable to diverse data ecosystems.

Atlan differentiates itself with its active metadata engine that automatically propagates governance tags across lineage paths. Instead of requiring manual stewardship, Atlan pushes metadata context back into the tools teams already use, including Snowflake, Databricks, Looker, and Power BI. This bidirectional sync approach reduces the manual overhead typical of traditional catalog-first platforms.

Collibra focuses heavily on governance workflows, policy management, and compliance automation. Its platform is designed for organizations where regulatory requirements demand strong audit trails, data access controls, and provable compliance documentation.

Great Expectations and Elementary take narrower but deeper approaches. Great Expectations focuses purely on data validation through codified expectations, while Elementary is purpose-built for the dbt ecosystem, providing observability and anomaly detection that integrates directly into dbt workflows.

Pricing Comparison

OpenMetadata is free and open-source under the Apache 2.0 license, making it one of the most cost-effective options for organizations with the engineering resources to self-host and maintain the platform. The managed service version is available through Collate, the company founded by OpenMetadata's creators.

Alation does not publicly disclose its pricing. Additional costs for connectors, governance modules, professional services, and user license scaling can push the total cost of ownership significantly higher. Implementation timelines are commonly cited as ranging from 3 to 9 months.

DataHub's open-source core is free to self-host under the Apache 2.0 license. DataHub Cloud, their managed offering, uses a tiered subscription model with pricing available upon contacting their sales team. The open-source version requires dedicated engineering resources for setup, hosting, and ongoing maintenance.

Atlan offers a freemium model. External sources position Atlan as a lower-cost alternative to Alation, with faster deployment timelines typically measured in days to weeks rather than months.

Collibra is an enterprise-priced platform. External comparisons position Collibra's pricing in a similar range to Alation, with implementation timelines of 6 to 12 months for full deployments.

Elementary offers a free tier for a single user, with a Pro plan and a Business plan for expanded team access. Metaplane follows a similar freemium model with paid tiers for additional users and features. Great Expectations is free and open-source, with paid upgrades available for additional capabilities.

When to Consider Switching

Consider switching from OpenMetadata if your organization needs a fully managed, enterprise-grade platform with dedicated support and does not have the engineering resources to self-host and maintain an open-source deployment. Alation or Collibra may be appropriate if regulatory compliance and enterprise governance workflows are critical requirements and budget allows for significant annual investment.

Consider DataHub if you want to stay in the open-source ecosystem but need a platform with a sizable community adoption footprint and a commercial managed service option. DataHub's architecture may also be preferable if you need highly extensible metadata modeling with custom entity types.

Atlan is worth evaluating if you are running a modern cloud-native data stack with Snowflake, Databricks, and dbt, and want an active metadata platform that reduces manual stewardship overhead through automated tag propagation and bidirectional integrations.

If your primary concern is data quality and validation rather than full metadata management, Great Expectations or Elementary may be more targeted solutions. Elementary is particularly strong for teams already using dbt as their transformation layer.

Stick with OpenMetadata if you value open-source flexibility, want a unified platform covering discovery through governance, have engineering capacity for self-hosting, and want to avoid vendor lock-in. OpenMetadata's streamlined four-component architecture and Apache 2.0 license provide long-term flexibility that proprietary alternatives cannot match.

Migration Considerations

Migrating from OpenMetadata to another platform involves several key considerations. First, evaluate your existing metadata integrations -- OpenMetadata supports 120+ native connectors, and you need to verify that your target platform supports all the data sources in your ecosystem. Second, consider the metadata you have already curated, including descriptions, tags, ownership assignments, and data quality rules. Exporting and reimporting this curated metadata is often the most labor-intensive part of any migration.

For organizations moving to DataHub, the migration path is relatively straightforward since both platforms share an open-source philosophy and Apache 2.0 licensing. Both support similar connector ecosystems and metadata standards, though the internal data models differ and will require mapping.

Migrating to commercial platforms like Alation or Collibra typically involves a longer implementation cycle. These platforms often require professional services engagement for architecture design, connector setup, workflow customization, and training. Budget for both the licensing costs and the implementation timeline when planning the transition.

If you are considering Atlan, its faster deployment timeline and modern integration approach may reduce migration friction, particularly for cloud-native data stacks. Atlan's bidirectional sync capabilities can help maintain consistency during a phased migration.

For teams moving to specialized tools like Great Expectations or Elementary, the migration is less about replacing OpenMetadata entirely and more about supplementing or replacing specific capabilities. Many organizations run a metadata catalog alongside dedicated data quality or observability tools as part of a composable data stack strategy.

Public signals

About these signals

Verified factual signals from public sources. They indicate observable activity or interest, not total adoption, product quality, or cost.

2.3k GitHub commits 90d15.4k GitHub stars0 vulnerabilities across 1 packageOpenSSF score 4.9/10

See all signals from 6 sources
Source
Signals
Last updated
GitHub
Commits 90d:2.3k↑128Stars:15.4k↑67
October 12, 2026
Docker Hub
Pulls:5.4M↑79.7k
October 12, 2026
PyPI
Weekly downloads:41.4k↑1.3k
October 12, 2026
Google Trends
Search interest:Top 46%overall
October 12, 2026
OSV
Package vulnerabilities:0 vulnerabilitiesacross 1 package

PyPI · openmetadata-ingestion@2.0.5.0

October 12, 2026
OpenSSF
Security score:4.9/10

github.com/open-metadata/OpenMetadata

October 12, 2026

Frequently asked questions

What is OpenMetadata?

OpenMetadata is an open-source data catalog and governance platform that helps organizations manage their metadata and improve data quality.

Is OpenMetadata free?

Yes, OpenMetadata is completely free to use, as it follows an open-source pricing model.

How does OpenMetadata compare to AWS Lake Formation?

OpenMetadata and AWS Lake Formation are both data governance platforms, but OpenMetadata is open-source and more flexible in terms of deployment options.

Can I use OpenMetadata for data discovery and cataloging?

Yes, OpenMetadata is designed to help organizations discover, catalog, and govern their data assets across multiple sources and systems.

What are the system requirements for installing OpenMetadata?

For the documented local Docker quickstart, OpenMetadata requires Docker 20.10.0 or newer, Docker Compose 2.1.1 or newer, 6 GiB of memory, and 4 vCPUs; the supplied Compose files support MySQL or PostgreSQL.

Is OpenMetadata suitable for large-scale enterprise data governance?

Yes, OpenMetadata is designed to handle large volumes of metadata and support complex data governance scenarios in enterprise environments.

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